Imperfect Cognitions

In this post Mark Miller (Center for Human Nature, Artificial Intelligence and Neuroscience, Hokkaido University) reports on a workshop Emotion and Prediction , which was held online on March 31- April 1, 2021.     Emotion permeates all mental life - it reflects our adaptivity, it imbues our activities and our environments with meaning and purpose, and it motivates and modulates our behaviours. We are emotional creatures through and through. While there has been a tremendous amount of work done on this topic, to date an integrative account capable of unifying the various theoretical perspectives and experimental results is still lacking. A recent workshop Emotion and Prediction brought together philosophers, cognitive scientists and machine learning researchers to explore the implications of a leading new framework emerging within computational neuroscience for the study of feelings, emotions and moods. The Predictive Processing (or Active Inference) framework starts from the vision

This post is by Michael Connors and Peter Halligan. Here they discuss their recent paper entitled 'Delusions and theories of belief' that was published in Consciousness and Cognition . Michael Connors is a research associate in the Centre for Healthy Brain Ageing at the University of New South Wales. Peter Halligan is an honorary professor in the School of Psychology at Cardiff University.  Michael Connors One approach to understanding cognitive processes is through the systemic study of its deficits. Known as cognitive neuropsychology, the study of selective deficits arising from brain damage has provided a productive way of identifying underlying cognitive processes in many well-circumscribed abilities, such as reading, perception, attention, and memory. Peter Halligan The application of these methods to higher-level processes has been more contentious. Known as cognitive neuropsychiatry, researchers over the past 30 years have applied similar methods to studying delusions –

Today blog post is by Clara Humpston , a Research Fellow at the Institute for Mental Health, University of Birmingham. She summarises her most recent paper co-authored with Matthew Broome in The Lancet Psychiatry . This paper is freely accessible upon registration on the Lancet’s website. Clara Humpston We aimed to discuss the history and concepts of self-disturbance in relation to the pathophysiology and subjective experience of schizophrenia in terms of three approaches: 1. The perceptual anomalies approach of the Early Heidelberg School of Psychiatry (with the kind help from Professor Aaron Mishara); 2. The more recent ipseity model by Louis Sass and Josef Parnas; and 3. The predictive coding framework rooted in computational psychiatry. Despite their importance, there has been a notable absence of efforts to compare them and to consider how they might indeed work together. Self-disturbances are transformations of basic self that form the inseparable background agai

This is the first in a series of two posts by Phil Corlett (pictured above) and Sarah Fineberg (pictured below). Phil and Sarah are both based in the Department of Psychiatry at Yale University. In this post and the next they discuss the adaptive value of delusional beliefs via their predictive coding model of the mind, and the potential delusions have for epistemic benefits (see their recent paper ' The Doxastic Shear Pin: Delusions as Errors of Learning and Memory ', in Cognitive Neuropsychiatry). Phil presented a version of the arguments below at the Royal College of Psychiatrists' Annual Meeting in Birmingham in 2015, as part of a session on delusions sponsored by project PERFECT . The predictive coding model of mind and brain function and dysfunction seems to be committed to veracity; at its heart is an error correcting, plastic, learning mechanism that ought to maximize future rewards, and minimize future punishments like the agents of traditional microec

This is our third of a series of posts in the papers published in an issue of Avant on Delusions. Here  Rick Adams  summarises his paper (co-written with Harriet R. Brown and Karl J. Friston ) ' Bayesian Inference, Predictive Coding and Delusions '. I am in training to become a psychiatrist. I have also recently completed a PhD at UCL under Prof Karl Friston , a renowned computational neuroscientist. I am part of a new field known as Computational Psychiatry (CP). CP tries to explain how various phenomena in psychiatry could be understood in terms of brain computations (see also Corlett and Fletcher 2014 , Montague et al.,  2012 , and Adams et al. forthcoming in JNNP ). One phenomenon that ought to be amenable to a computational understanding is the formation of both ‘normal’ beliefs (i.e. beliefs which are generally agreed to be reasonable) and delusions. There are strong theoretical reasons to suppose that we (and other organisms) form beliefs in a Bayesian way

This is a response to Max Coltheart's post (and comments) , posted on behalf of Phil Corlett. Phil Corlett As usual Max, a thorough, interesting and well written piece. I am curious about a couple of things. First, you say that the prediction error signal fails in our model. Are you implying that we believe delusions form in the absence of prediction error? Our data point to the opposite case. Prediction errors are inappropriately engaged in response to events that ought not to be surprising. That is why people with delusions learn about events (stimuli, thoughts, percepts) that those without delusions would ignore. Second, you claim that prediction error is key to belief updating in your model. Are you aligning prediction error with Factor 1 or Factor 2? It seems Factor 1, but I wanted to check – particularly since you align Factor 2 with the functioning of right dorsolateral prefrontal cortex, which, as you know, we’ve implicated in prediction error signaling with our

Over the last few years I have worked more and more on the idea that the brain is a prediction error minimizer. This has now resulted in a book— The Predictive Mind —just published with Oxford University Press. The Predictive Mind By Jakob Hohwy The first part of the book explains the basic idea of prediction error minimization, which mainly stems from work by Karl Friston and others in computational neuroscience. The second part applies this to the binding problem, to cognitive impenetrability, to delusions and autism, and to a range of philosophical questions about misrepresentation. The third part considers how it applies to attention, consciousness, the mind-world relation, and the nature of self. The prediction error minimization idea says that all the brain ever does is minimize the error of predictions about its sensory input, formed on the basis of an internal model of the world and the body. The better these predictions are, the less error there is. On this view, the

Phil Corlett I’m interested in beliefs. Specifically, how the brain is involved in normal and abnormal belief formation. For example, I study delusions, the often bizarre and fixed false beliefs that characterize serious mental illnesses like schizophrenia.  I’m a cognitive neuroscientist, which means I use data from brains to make inferences about minds.    I take what many consider to be a radically reductionist approach to beliefs. I think they might be related to simple behaviors like Pavlovian and instrumental learning. These processes can be observed in very simple organisms and I try to apply what we know about them to study beliefs. Central to our understanding of learning, and I argue belief formation, is the concept of salience or importance. We learn and remember information about important events so we can respond appropriately if the same circumstances recur in the future. I think beliefs are one way that such learning and memory is manifest. If beliefs a